import gradio as gr
from TTS.api import TTS

model_id = "tts_models/multilingual/multi-dataset/xtts_v1"
device = "cuda"

tts = TTS(model_id)
tts.to(device)


def predict(prompt, audio_file_pth):
    
    tts.tts_to_file(
        text=prompt,
        file_path="output.wav",
        speaker_wav=audio_file_pth,
        language=language,
    )

    return (
        gr.make_waveform(
            audio="output.wav",
        ),
        "output.wav",
    )


title = "Coquib🐸 XTTS - Spanish Demo"

description = """
<a href="https://huggingface.co/coqui/XTTS-v1">XTTS</a> is a Voice generation model that lets you clone voices into different languages by using just a quick 3-second audio clip. 
<br/>
Built on Tortoise, XTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy. 
<br/>
This is the same model that powers Coqui Studio, and Coqui API, however we apply a few tricks to make it faster and support streaming inference.
<br/>
<br/>
<p>For faster inference without waiting in the queue, you should duplicate this space and upgrade to GPU via the settings.
<br/>
<a href="https://huggingface.co/spaces/coqui/xtts?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
</p>
"""

article = """
<div style='margin:20px auto;'>
<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
</div>
"""

gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(
            label="Texto",
            info="Una o dos frases es suficiente-",
            value="Clibrain es una empresa que desarrolla soluciones basadas en inteligencia artificial en español.",
        ),
        gr.Audio(
            label="Audio de referencia",
            info="Haz clic en el botón ✎ para subir tu propio audio o del hablante objetivo",
            type="filepath",
            value="examples/female.wav",
        ),
    ],
    outputs=[
        gr.Video(label="Waveform Visual"),
        gr.Audio(label="Synthesised Audio"),
    ],
    title=title,
    description=description,
    article=article,
    examples=examples,
).queue().launch(debug=True)